Adding episodic memory retrieval to multi-agent reinforcement learning dramatically improves routing decisions in dynamic satellite networks, enabling agents to reuse successful past strategies without retraining.
RAMA is a routing system for satellite networks that combines reinforcement learning with memory retrieval. Instead of just learning from current observations, each satellite remembers successful routing decisions and retrieves similar past experiences when making new routing choices. This approach reduces delivery delays by 13-93% compared to existing methods, especially as networks scale up.